This is a complete training example for BinaryNets using Binary-Backpropagation algorithm as explained in "Binarized Neural Networks: Training Deep Neural Networks with Weights and Activations Constrained to +1 or -1, Matthieu Courbariaux, Itay Hubara, Daniel Soudry, Ran El-Yaniv, Yoshua Bengio' on following datasets: Cifar10/100, SVHN, MNIST
##Data We use dp library to extract all the data please view installation section
##Dependencies
- Torch (http:https://torch.ch)
- "DataProvider.torch" (https://github.com/eladhoffer/DataProvider.torch) for DataProvider class.
- "cudnn.torch" (https://github.com/soumith/cudnn.torch) for faster training. Can be avoided by changing "cudnn" to "nn" in models.
- "dp" (https://github.com/nicholas-leonard/dp.git) for data extraction
To install all dependencies (assuming torch is installed) use:
luarocks install https://raw.githubusercontent.com/eladhoffer/eladtools/master/eladtools-scm-1.rockspec
luarocks install https://raw.githubusercontent.com/eladhoffer/DataProvider.torch/master/dataprovider-scm-1.rockspec
luarocks install dp##Training You can start training using:
th Main_BinaryNet_Cifar10.lua -network BinaryNet_Cifar10_Model
or,
```lua
th Main_BinaryNet_MNIST.lua -network BinaryNet_MNIST_Model ##Additional flags
| Flag | Default Value | Description |
|---|---|---|
| modelsFolder | ./Models/ | Models Folder |
| network | Model.lua | Model file - must return valid network. |
| LR | 0.1 | learning rate |
| LRDecay | 0 | learning rate decay (in # samples |
| weightDecay | 1e-4 | L2 penalty on the weights |
| momentum | 0.9 | momentum |
| batchSize | 128 | batch size |
| stcNeurons | true | using stochastic binarization for the neurons or not |
| stcWeights | false | using stochastic binarization for the weights or not |
| optimization | adam | optimization method |
| runningVal | true | use running mean and std |
| epoch | -1 | number of epochs to train (-1 for unbounded) |
| threads | 8 | number of threads |
| type | cuda | float or cuda |
| devid | 1 | device ID (if using CUDA) |
| load | none | load existing net weights |
| save | time-identifier | save directory |
| dataset | Cifar10 | Dataset - Cifar10, Cifar100, STL10, SVHN, MNIST |
| dp_prepro | false | preprocessing using dp lib |
| whiten | false | whiten data |
| augment | false | Augment training data |
| preProcDir | ./PreProcData/ | Data for pre-processing (means,Pinv,P) |